Optimised tracking of a region of a patient&#39;s body

ABSTRACT

Disclosed herein is a medical device for tracking movement of a region of a patient&#39;s body. The region has a range of motion, for example a range of respiratory motion. The device comprises a controller configured to determine a motion of the region based on one or more initial images depicting at least part of the region. The controller is further configured to predict, based on the determined motion, a motion event time at which at least one property associated with the motion or position of the region will meet at least one criterion, wherein the at least one criterion comprises the region being located at a particular point in its range of motion. The controller is further configured to determine, based on the predicted motion event time, at least one subsequent image capture time at which at least one subsequent image should be captured.

This disclosure relates to a medical device, such as a radiotherapydevice, and in particular to a medical device and related methods fortracking a region of a patient's body.

BACKGROUND

Radiotherapy can be described as the use of ionising radiation, such asX-rays, to treat a human or animal body. Radiotherapy is commonly usedto treat tumours within the body of a patient or subject. In suchtreatments, ionising radiation is used to irradiate, and thus destroy ordamage, cells which form part of the tumour.

Modern radiotherapy treatment uses techniques to reduce the radiationdose given to healthy tissue, to thereby provide a safe treatment. Forexample, a standard approach to minimising a radiation dose received byhealthy tissue surrounding a target region is to direct the radiationtowards the target region from a plurality of different angles, forexample by rotating a source of radiation around the patient by use of arotating gantry. In this way, a cumulative radiation dose may be builtup at the target region. However, since the radiation is applied from aplurality of different angles, the same, high, cumulative radiation doseis not built up in the healthy tissue.

However, patient movement during treatment such as breathing, coughing,swallowing, etc. can result in movement of the tumour. Such motionsduring a treatment session may be referred to as intrafractionalmotions, and these motions can affect the dose applied to differentregions of the patient's body. In other words, movement of the patientduring radiotherapy treatment can reduce the effectiveness of thetreatment by reducing the dose applied to the tumour, and may causedamage to healthy tissue.

Known techniques to address the problems caused by intrafractionalmotions include training a patient's breathing, or asking the patient tohold their breath during radiotherapy treatment. In such techniques, thepatient's breathing is adjusted based on the requirements of thetreatment. However, this may be uncomfortable or impossible for certainpatients, and often restricts the time during which radiation can beapplied.

Techniques which do not involve training a patient's breathing includerespiratory gating and tracking techniques. Respiratory gatingtechniques may involve dividing a patient's breathing cycle into two ormore regions, e.g. a first, optimal region in which radiation should beapplied, and a second, suboptimal region in which radiation should notbe applied for safety reasons. In response to sensing that the patient'sbreathing cycle has entered the suboptimal region, the application ofradiation is halted (gated). Respiratory gating techniques involvemaking assumptions about the relationship between the movement of thepatient's body throughout their respiratory cycle and the movement ofthe tumour. Information about the movement of the tumour may be acquiredby extrapolating from surrogate respiration signals, which may beacquired by observing the movement of the patient's chest using cameras,or using external markers or strain gauges applied directly to thepatient. However, the correlation between the surrogate signal andmovement of the tumour may not be accurate.

Tracking techniques involve adjusting the direction, shape, or otherproperties of the therapeutic beam based on the movement of the tumour.For example, the leaves of a multi-leaf collimator may be moved in orderto adjust the shape of the treatment beam in accordance with movement ofthe tumour. However, in order to make use of tracking techniques,information about the movement of the target region must be acquired.Radiotherapy devices may be equipped with an imaging apparatus and, forexample, four-dimensional computed tomography (4DCT) may be used totrack the tumour or target region. This imaging modality may be used totrack the tumour with acceptable accuracy, but the imaging modality hasa finite acquisition time. This means that the target region position asdetermined by 4DCT is an average position of the tumour during theacquisition time. Tracking techniques based on 4DCT therefore have thepotential to introduce minor inaccuracies to a patient's treatment whenused to control radiotherapy.

To date, tracking a target region based on 2D (projection) images takenby the KV imager has not been adopted for the purpose of tracking atarget region, because it has been thought that it would be necessary totake a large number of KV images, at short regular intervals, in orderto track the target region with sufficient accuracy. This would increasethe dose received by the patient to unacceptable levels.

The present invention seeks to address these and other disadvantagesencountered in the prior art.

SUMMARY

An invention is set out in the accompanying claims. Aspects of theinvention are set out in the independent claims, with optional featuresbeing set out in the dependent claims.

According to an aspect, a medical device for tracking movement of aregion of a patient's body is provided. The region has a range ofmotion, for example a range of respiratory motion. The device comprisesa controller configured to determine a motion of the region based on oneor more initial images depicting at least part of the region. Thecontroller is further configured to predict, based on the determinedmotion, a motion event time at which at least one property associatedwith the motion or position of the region will meet at least onecriterion, wherein the at least one criterion comprises the region beinglocated at a particular point in its range of motion. The controller isfurther configured to determine, based on the predicted motion eventtime, at least one subsequent image capture time at which at least onesubsequent image should be captured.

According to another aspect, a method for tracking a region of apatient's body is provided. The region has a range of motion, forexample a range of respiratory motion. The method comprises receiving aplurality of images, each of the plurality of images depicting at leastpart of the region and each of the plurality of images having been takenat a respective image capture time. The method further comprisesdetermining, based on the plurality of images and the respective imagecapture times, a motion of the region between the image capture times;predicting, based on the determined motion, a motion event time at whichat least one property associated with the motion will meet at least onecriterion. The at least one criterion comprises the region being locatedat a particular point in its range of motion. The method furthercomprises determining, based on the predicted motion event time, atleast one subsequent image capture time at which a subsequent imageshould be captured.

According to another aspect, a computer readable medium is provided. Themedium comprises computer executable instructions which, when executedby the computer, cause the computer to receive a plurality of images,each of the plurality of images depicting at least part of the regionand each of the plurality of images having been taken at a respectiveimage capture time; determine, based on the plurality of images and therespective image capture times, a motion of the region between the imagecapture times; predict, based on the determined motion, a motion eventtime at which at least one property associated with the motion will meetat least one criterion; and determine, based on the predicted motionevent time, at least one subsequent image capture time at which asubsequent image should be captured.

According to another aspect, a medical device for tracking movement ofan internal region of a patient's body is disclosed. The devicecomprises a controller configured to generate a patient movement modelbased on a plurality of images depicting at least part of the region,the plurality of images being taken using an imaging apparatuscomprising a source of imaging radiation; and a surrogate signalindicative of movement of the patient's surface anatomy. The patientmovement model enables estimation of the position of the region as afunction of the surrogate signal and/or as a function of time. Thecontroller is also configured to: estimate, using the patient movementmodel, a motion event time at which the position of the region will meetat least one criterion; and determine, based on the predicted motionevent time, at least one subsequent image capture time at which at leastone subsequent image should be captured using the imaging apparatus.

According to another aspect, a method for tracking movement of aninternal region of a patient's body is disclosed. The method comprisesgenerating a patient movement model based on: a plurality of imagesdepicting at least part of the region, the plurality of images beingtaken using an imaging apparatus comprising a source of imagingradiation; and a surrogate signal indicative of movement of thepatient's surface anatomy. The patient movement model enables estimationof the position of the region as a function of the surrogate signaland/or as a function of time. The method further comprises estimating,using the patient movement model, a motion event time at which theposition of the region will meet at least one criterion; anddetermining, based on the predicted motion event time, at least onesubsequent image capture time at which at least one subsequent imageshould be captured using the imaging apparatus.

According to another aspect, a computer readable medium is provided. Themedium comprises computer executable instructions which, when executedby the computer, cause the computer to generate a patient movement modelbased on a plurality of images depicting at least part of the region,the plurality of images being taken using an imaging apparatuscomprising a source of imaging radiation; and a surrogate signalindicative of movement of the patient's surface anatomy. The patientmovement model enables estimation of the position of the region as afunction of the surrogate signal and/or as a function of time. Thecontroller is also configured to: estimate, using the patient movementmodel, a motion event time at which the position of the region will meetat least one criterion; and determine, based on the predicted motionevent time, at least one subsequent image capture time at which at leastone subsequent image should be captured using the imaging apparatus.

Features of the various aspects can be used interchangeably, asappropriate, and this would be understood by the skilled person.

FIGURES

Specific embodiments are now described, by way of example only, withreference to the drawings, in which:

FIGS. 1 a-e depict a radiotherapy device or apparatus according to thepresent disclosure.

FIG. 2 a depicts a graph showing the timing of KV images with tumourmovement according to a prior tracking method, and FIG. 2 b depicts agraph showing the timing of KV images with tumour movement according tothe present method.

FIG. 3 depicts a disclosed method.

FIGS. 4 a and 4 b depict the timing of images based on different motionevents, according to the present disclosure.

FIG. 5 depicts a control diagram according to the present disclosure.

FIGS. 6 a and 6 b depict the movement of an object between a first timeat which a first image is taken and a second time at which a secondimage is taken;

FIG. 7 depicts a disclosed method.

DETAILED DESCRIPTION

The present application relates to methods for tracking the motion of anobject or item. The methods may be used for any application scenario inwhich the tracking of an item or object is useful. The method isparticularly useful for tracking a target region of a patient's body,for example for diagnostic purposes. The present application alsorelates to a medical device capable of performing the disclosed methods.

A particularly beneficial use of the present methods is theirapplication to the field of radiotherapy. Image-guided radiotherapy(IGRT) can be described as the use of imaging as part of radiotherapy toimprove the precision and accuracy of treatment delivery. Modernradiotherapy devices typically comprise an imaging apparatus. Images maybe taken immediately before treatment begins, which may then be used toposition the patient on the table accurately with respect to a treatmentplan. Images may even be taken during treatment, which may be used toupdate the radiotherapy treatment. IGRT may therefore be used to treattumours while taking into account movement of the patient. This isparticularly important for tumours which change position as the patientbreathes. Using tracking techniques it is possible to, for example, haltthe application of radiation when the images indicate a significantvolume of the tumour is no longer directly in the path of the beam oftherapeutic radiation.

Different imaging modalities may be used as part of IGRT, for exampleultrasound and MRI. Ultrasound and MRI are non-ionising modalities andtherefore do not give a radiation dose to the patient. However, KVimaging makes use of ionising radiation to obtain an image. For thisreason, to date, kV images have not been adopted in tracking within IGRTas it has been thought that to do so would pass on a significant dose ofradiation to the patient.

Reference is made to FIG. 2 a , which is a graph showing an imageacquisition scheme according to the only manner in which it has beenthought, to date, that kV images could be incorporated into a trackingtechnique suitable for use in radiotherapy. The graph shows the movementof a tumour over time, with time along the x axis and position along they axis. The curved line depicts the motion of the tumour as the patientmoves. The majority of this movement is associated with the patient'srespiration cycle, and hence the curve has a rough periodicity. Thevertical lines indicate times at which kV images are taken. Theacquisition frequency, or frame rate, of the kV imaging is regular, andreasonably high, in order to ensure that a potentially important changein tumour position or change in the tumour motion is not missed. This isparticularly important if the resulting kV images are to be used toinform and update a radiotherapy treatment plan. A problem with thistechnique, however, is that the dose given to the patient isunacceptably high.

Reference is now made to FIG. 2 b , which is a similar schematic graphto that of FIG. 2 a and shows the movement of a tumour over time.However, here, the acquisition of kV images is controlled by methods ofthe present disclosure. Instead of kV images being taken regularly andat a high frame rate, motion estimation techniques are used in order toinform when the acquisition of a kV image should occur. Based onpreviously acquired kV images, motion estimation techniques are used topredict when a motion event will occur. For example, one or more motionvectors may be determined for the tumour based on previously acquired kVimages, and these motion vectors may be used to predict the futuremotion of the tumour. Motion estimation techniques can be used topredict a time at which a motion event will occur. For example, motionestimation techniques may be employed to determine a future time atwhich the tumour will halt motion and become stationary, changedirection, or else will be at a particular predetermined location in thepatient's body. The next kV image can then be sequenced based on thispredicted future event time. For example, it may be determined that thenext kV image should be taken just before, at, or just after thepredicted motion event time. Accordingly, the present methods allowoptimal use to be made of fewer kV images, because the images are onlytaken at times judged to be the most useful and impactful to thetracking process. Accordingly, the dose to the patient from the kVimages is significantly reduced, while a high response time ismaintained for any changes in tumour motion.

While reference is made to tracking a tumour, any region of thepatient's body may be tracked. For example, organs at risk (OARs) areregions of tissue which should receive a minimal dose of radiationduring radiotherapy for clinical reasons. OARs may, for example, be inthe vicinity of the tumour, and an important part of radiotherapy is thedelineation of OARs and the management of radiation dose to thesetissues. While present methods may be used to track a tumour to ensureit is kept within the path of a treatment beam, or else to halt theapplication of radiation when the tumour exits the path of the treatmentbeam, present methods may equally apply to tracking an OAR to ensure itdoes not enter the path of the treatment beam.

Methods of the present disclosure can be carried out on a medical devicecomprising an imaging apparatus, for example a radiotherapy device. FIG.1 a shows a suitable radiotherapy device 100. The device 100 can bedescribed as an Image Guided Radiotherapy (IGRT) machine. The IGRTmachine 100 comprises a rotatable gantry 102 to which are affixed atreatment apparatus 104 and an imaging apparatus 106. In this example,the treatment apparatus 104 and the imaging apparatus 106 are attachedto the gantry, so that they are rotatable with the gantry, i.e. so thatthey rotate as the gantry rotates. Positioned in a treatment volume ofthe device is a couch 110 upon which a patient 112 lies duringradiotherapy treatment.

Treatment apparatus 104 comprises a treatment beam source 114 and atreatment beam target 116. The treatment beam source 114 is configuredto emit or direct therapeutic radiation, for example MV energyradiation, towards patient 112. As the skilled person will appreciate,the treatment beam source 114 may comprise an electron source, a linacfor accelerating electrons toward a heavy metal, e.g. tungsten, targetto produce high energy photons, and a collimator configured to collimatethe resulting photons and thus produce a treatment beam. Once thetreatment radiation has passed from the source 114 and through thepatient 112, the radiation continues towards treatment beam target 116,where it is blocked/absorbed. The treatment beam target 116 may includean imaging panel (not shown). The treatment beam target may thereforeform part of an electronic portal imaging device (EPID). EPIDs aregenerally known to the skilled person and will not be discussed indetail herein.

Imaging apparatus 106, otherwise known as an imaging system 106,comprises an imaging beam source 118 and an imaging panel 120. Theimaging beam source 118 is configured to emit or direct imagingradiation, for example X-rays/kV energy radiation, towards the patient112. As the skilled person will appreciate, the imaging beam source 118may be an X-ray tube or other suitable source of X-rays. The imagingbeam source 119 is configured to produce kV energy radiation. Once theimaging radiation has passed from the imaging beam source 118 andthrough the patient 112, the radiation continues towards imaging panel120. The imaging panel 120 may be described as a radiation detector, ora radiation intensity detector. The imaging panel 120 is configured toproduce signals indicative of the intensity of radiation incident on theimaging panel 120. In use, these signals are indicative of the intensityof radiation which has passed through a patient 112. These signals maybe processed to form an image of the patient 112. This process may bedescribed as the imaging apparatus 106 and/or the imaging panel 120capturing an image. By taking images at multiple angles around thepatient it is possible to produce a 3D image of the patient, for exampleusing tomographic reconstruction techniques.

In the illustrated example, the treatment apparatus 104 and imagingapparatus 106 are mounted on the gantry such that a treatment beamtravels in a direction that is generally perpendicular to that of theimaging beam.

Because the gantry 102 is rotatable, the treatment beam can be deliveredto a patient from a range of angles. Similarly, the patient can beimaged from a range of angles. See for example FIGS. 1 b-2 e , each ofwhich shows the gantry 102 of FIG. 1 a at a different rotation angle. InFIG. 1 b , the gantry is positioned at a ‘first’ gantry rotation angle,in which the treatment source 114 directs the treatment beam towards thepatient in a vertical/downwards direction and in which the imagingsource 118 directs the imaging beam towards the patient in ahorizontal/right-to-left’ direction. In FIG. 1 c , the gantry has beenrotated 45-degrees clockwise, into a ‘second’ rotation angle. In FIG. 1d , the gantry has been rotated a further 45-degrees clockwise (i.e.90-degrees clockwise relative to FIG. 1 b ) into a ‘third’ rotationangle, so that the treatment source 114 directs the treatment beamtowards the patient in a horizontal/‘right-to-left’ direction and inwhich the imaging source 118 directs the imaging beam towards thepatient in a vertical/upwards direction. Finally, in FIG. 1 e , thegantry has been rotated a further 45-degrees clockwise (i.e. 135-degreesclockwise relative to FIG. 1 b ) into a ‘fourth’ rotation angle.

As the skilled person will appreciate, the gantry 102 can be rotated toany of a number of discrete angular positions relative to a patient. Thetreatment apparatus 104 may direct radiation toward the patient at eachor a number of these discrete angular positions, according to atreatment plan. The treatment apparatus 104 may even be used tocontinuously irradiate a patient at all rotation angles as it is rotatedby the gantry 102. The angles from which radiation is applied, and theintensity and shape of the therapeutic beam, may depend on a specifictreatment plan pertaining to a given patient.

The device 100 may additionally comprise a surface scanning system. Asurface scanning system is a system configured to scan a patient whilethe patient is positioned on the patient positioning surface. In anexample, the surface scanning system may comprise a processor, a patternprojector, and at least one camera or other detecting means. The patternprojector is configured to project a 2D or 3D pattern onto the patientfrom above. The camera, or a plurality of cameras, observes the patternprojected onto the patient, and the processor is configured todetermine, based on the detected/observed projected pattern, informationassociated with the position of the patient. Surface scanning systemsare known in the art and may be used to track motion of the patient. Thesurface scanning system may be used to determine a surfacerepresentation based on detection of the projected 2D or 3D pattern ontothe patient. The surface representation may be described as a computermodel which represents movement of the patient. The surfacerepresentation algorithm may be any of a feature, point or model-basedmethod, or a technique based on global similarity. Such algorithms areknown to the skilled person.

Alternatively, the surface scanning system needn't comprise a patternprojector, but may instead monitor the movement of the patient's surfaceanatomy using a camera and known video analysis techniques.

The device 100 additionally comprises a controller (not shown). Thecontroller is a processor or a processing device configured to controlthe device 100. The controller is coupled with a computer readablestorage medium comprising or storing instructions which, when executedby the controller, cause the controller to carry out any method ormethods disclosed herein. Accordingly, the approaches described hereinmay be embodied on the computer-readable medium, which may be anon-transitory computer-readable medium. The computer-readable mediumcarrying computer-readable instructions arranged for execution upon aprocessor so as to make the processor carry out any or all of themethods described herein.

The term “computer-readable medium” as used herein refers to any mediumthat stores data and/or instructions for causing a processor to operatein a specific manner. Such storage medium may comprise non-volatilemedia and/or volatile media. Non-volatile media may include, forexample, optical or magnetic disks. Volatile media may include dynamicmemory. Exemplary forms of storage medium include, a floppy disk, aflexible disk, a hard disk, a solid state drive, a magnetic tape, or anyother magnetic data storage medium, a CD-ROM, any other optical datastorage medium, any physical medium with one or more patterns of holes,a RAM, a PROM, an EPROM, a FLASH-EPROM, NVRAM, and any other memory chipor cartridge.

The controller is configured to send control signals to multipledifferent components of the device 100, for example those describedabove and elsewhere herein. In particular, the controller is configuredto send instructions to the imaging apparatus in order to instruct thatan image be taken at a particular time, and/or is configured to updateimage sequencing information such that the times at which images arecaptured can be controlled by the controller. The controller is alsoconfigured to send control signals to the treatment apparatus in orderto effect changes in radiotherapy treatment. The controller alsocollects data indicative of the performance and actions of variouscomponents of the device 100. For example, the controller controlsrotation of the gantry and records the angle to which the gantry hasbeen rotated.

FIG. 3 is a flowchart depicting a method 300 of tracking a region of apatient's body according to the present disclosure. FIGS. 4 a and 4 bshow graphs 400 a, 400 b. These graphs show displacement of the regionof the patient's body on the y axis with time along the x axis. Unbrokenvertical lines depict times at which images are taken. Dashed verticallines indicate times at which it has been predicted that a motion eventwill occur. FIGS. 4 a and 4 b show images being taken according todifferent acquisition schemes, which are created using different imagecapture criteria, as will be explained below. Reference will be made toFIGS. 4 a and 4 b as appropriate in the following description of theflowchart of FIG. 3 .

While displacement is depicted in a single direction on the graphs forsimplicity, it should be appreciated that motion of the region may takeplace in three dimensions and therefore displacement may be a threedimensional vector quantity. Also, while reference is made todisplacement, the disclosed methods may equally make use of a scalarposition. The graphs shown in FIGS. 2 a, 2 b, 4 a and 4 b describe thechange in position of a region of a patient's body with time.

At block 301, one or more images of a region of a patient's body arereceived. The one or more images may be a plurality of images. Eachimage depicts at least part of the region. Each of the images has itsown associated image capture time, as can be appreciated from FIGS. 4 aand 4 b . The images may have been obtained via an imaging apparatusconfigured to emit imaging radiation, and/or ionising radiation. Theimages may be kV images, i.e. may have been obtained via a kV imagingapparatus such as an X-Ray or CT imaging apparatus. In an example, thereare two images comprised within the plurality of images: a first imagetaken at a first image capture time and a second image taken at a secondimage capture time. An example of a suitable plurality of images isdepicted in FIG. 4 a via reference numeral 402 a, and in FIG. 4 b viareference numeral 402 b. At block 302, the location of the region in theone or more images is determined. The location is determinedautomatically. This step can be performed using known auto-segmentationor auto-contouring techniques.

At block 303, a motion of the region is determined. The motion isdetermined based on the location of the region in the image(s)determined at block 302 along with the relevant image capture times. Themotion may be determined based on either a subset, or all, of the imagesreceived at block 301. A suitable subset may be the two most recentimages received at the time the determination at 303 is made. Thedetermined motion can be described as a motion between the image capturetimes of the images can be determined. In particular implementations,determining the motion comprises determining a parameter which describesthe motion of the region. A suitable parameter is a motion vector. A setof motion vectors may be determined, with each motion vector describingmotion of the region between two respective image capture times.

Determining the motion may comprise calculating or determining amathematical object or function which can be used to determine theposition or velocity of the region as a function of time, for examplecalculating a motion vector or a patient movement model.

Determining the motion may comprise determining the motion based on thelocation of the region of the patient's body in each of the receivedimages and the respective image capture times of each image. Using oneor more motion estimation techniques, at least one motion vector isdetermined for the region based on the images, e.g. based on the firstand the second image. The at least one motion vector comprises a vectorvelocity value. In other words, determining a motion of the region maycomprise determining both a direction of motion and a speed of motionfor the region. For example, in an implementation which makes use of twoimages (402 a, or 402 b) as shown in FIGS. 4 a and 4 b , the region ofthe patient can be identified in the first image and the second imageusing known image analysis or motion estimation techniques. Based on thedetermined positions of the region in the two images, a motion vector410 a, 410 b can be determined. For example, if the region has moved 25pixels between the first time and the second time, then the speed ofmovement can be simply calculated as 25 pixels/(t₂−t₁). For a medicaldevice comprise an imaging apparatus, the field of view of the images isknown to a high level of accuracy. As the field of view of the images isknown, it is also possible to represent this speed value in units suchas mm/s or cm/s. An example of determining a motion vector is describedin relation to FIGS. 6 a and 6 b.

In an example, motion estimation can be performed for each pixel in theimage received at block 301 in order to acquire a motion vector for eachpixel in the manner disclosed herein, for example with respect to FIGS.6 a and 6 b . A region growing method/algorithm can be applied in orderto determine which motions of nearby pixels are similar. Pixels whichare nearby one another (e.g. within a distance threshold) and which havea similar motion vector (e.g. within a motion threshold) may be groupedtogether and determined to be associated with the same region of thepatient's anatomy. This analysis can be based on a pre-segmentedoriginal image.

While reference has been made to determining one motion vector, it willbe appreciated that several motion vectors associated with motion of theregion may be determined. This is useful in situations where the regionis large and cannot be accurately described with a single motion vector,for example a long tumour which moves in different directions as thepatient breathes. Implementations in which multiple motion vectors arecalculated better take into account the fact that tissues in the bodyare not typically rigid and so do not move as solid objects. Fordeformable regions of the patient's body it is possible to track theregion using multiple motion vectors, which would describe deformationof the region as the patient moves. In an example where the intention isto track movement of the patient's heart, multiple motion vectors couldbe used. A motion vector which tracks movement of the centre of theheart may suggest that the heart is not exhibiting significant movementwith time, but one or more motion vectors associated with the walls andother cardiac tissue of the heart will give a better indication of themovement of the heart with time.

At block 304, optionally, radiotherapy treatment is updated. Treatmentmay be updated based on one, or both, of the location of the region inthe images determined at block 302 and the motion of the regiondetermined at block 303. Updating radiotherapy based on the location andmotion of the tumour or other target region in images taken duringtreatment is known and can be performed using known techniques. Thisensures the treatment adapts as the target position changes duringtreatment.

For example, the target may move due to movement of the patient such asbreathing, coughing, swallowing, etc. Such motions during a treatmentsession may be referred to as intrafractional motions. Updating theradiotherapy treatment may comprise adjusting one or more parameterswhich affect the delivery of radiation to the patient. For example, aprocessor may be configured to control at least one of the followingbased on the determined motion and/or the location of the region in theimages: a gating of a radiation beam; the shape of the beam viaadjusting a multi-leaf collimator (MLC); or a movement of a patientsupporting system.

Optionally, at block 305, a range of motion associated with the regionis determined. This may comprise determining range of motion data, whichis indicative of the range of motion which the region moves through. Thedetermination at block 305 may take place at any suitable time, forexample before or during treatment. The determination at block 305 maybe performed before any of blocks 301 and 303. FIGS. 4 a and 4 b showmovement of a tumour over time as it moves periodically (orsemi-periodically) through a range of motion 408 defined by a maximumand a minimum displacement value.

A number of methods can be used to determine the range of motion. Forexample, a second plurality of images may be obtained, from which therange of motion can be extracted (the use of ‘second’ is todifferentiate from a ‘first’ plurality of images which may be receivedat block 301). A suitable second plurality of images, from which rangeof motion data can be estimated/extracted, is depicted in FIG. 4 a byreference to the numeral 404. The range of motion 408 of the region isdepicted via the double-headed arrow that extends in the y-direction.The range of motion extends between the minimum and the maximumdisplacement of the tumour or other region of the patient's body. Theimages of the second plurality of images are taken as the region, e.g. atumour, moves through its range of motion. In the illustrative exampledepicted, the plurality of images comprises 6 images taken at variouspoints in the patient's breathing cycle. There may be overlap betweenthe first and second plurality of images such that the first pluralityof images 402 a (from which motion of the region may be determined) iswholly or partly encompassed by the second plurality of images 404 (fromwhich motion of the region may be determined). As can be appreciatedfrom FIG. 4 a , the first and second image which comprise the firstplurality of images 402 a also form part of the second plurality ofimages 404. By using the images of the first plurality of images 402 afor more than one purpose, i.e. to extract range of motion data and todetermine a motion of the region, optimal use of images is achieved andradiation dose to the patient is reduced. Determining the range ofmotion may comprise manually segmentation the images, or usingauto-segmentation techniques, to determine the location of the tumour ineach image, and then comparing the locations of the tumour in the imagesin order to determine the location extremes.

The range of motion data also be determined using a surface scanningsystem and associated methods. In an example, the patient may bepositioned on a patient position surface (otherwise known as a couch ortable) and imaged using the surface scanning system. Continuous scanningallows a surface representation/model of the patient to be generatedaccording to known algorithms, from which the range of motion data canbe extracted.

The range of motion data may comprise a minimum and a maximumdisplacement of the region. In other words, the range of motion data maycomprise the extremes of the motion of the region. In particular, if anaim of the method is to take images at particular points in a breathingcycle, the images are taken at particular points throughout thepatient's breathing cycle. From these images, a minimum and maximumdisplacement of the tumour throughout the patient's breathing cycle canbe extracted. The motion vectors, or average motion vectors, may also berecorded at the maximum and minimum displacement points.

It is possible to estimate/extract the range of motion of the regionusing known motion estimation and/or image analysis techniques. Therange of motion data may be the maximum and minimum displacement values.For example, co-ordinates of the maximum and minimum displacement valuesmay be determined and recorded and saved as range of motion data. Whilein the depicted example the plurality of images are obtained over onlyone movement cycle, it will be appreciated that images may be taken overa number of cycles.

In addition to the maximum and minimum displacement values, multipledisplacement values may be recorded over the full cycle of motion suchthat the range of motion data describes the displacement of the regionover the full cycle of motion.

It will be appreciated that, for a radiotherapy device with a rotatableimaging apparatus, the range of motion position values which make up therange of motion data should be recorded in a three-dimensional spacerather than, for example, with respect to the 2D surface of the imagingpanel.

The range of motion data may comprise maximum and minimum displacementsin each of a plurality of directions/dimensions. For example, for theregion of the patient, a maximum and minimum displacement may berecorded in an x, y, and z co-ordinate system within the treatment orimaging volume of the medical device. Such a co-ordinate system may bedescribed as a room co-ordinate.

The range of motion data may be described as a movement arc. Themovement arc may be comprised if displacement in each of a plurality ofdimensions, for example in some or all of x, y and z directions within athree-dimensional co-ordinate system used to define the treatment orimaging volume of the medical device.

Alternative methods of determining range of motion data comprise usingnon-ionising imaging modalities. For example, MR images of the patientmay be available from which the range of motion can be extracted.Alternatively, optical sensors, cameras or ultrasound techniques can beused to obtain the second plurality of images. It is beneficial for thesecond plurality of images to be taken just before the motion of theregion is to be tracked to ensure the range of motion data is accurateand indicative of the range of motion during treatment. Non-imagingtechniques may also be used to determine the range of motion/range ofmotion data, for example a respiratory strain gauge, a strain belt, oneor more sensors such as optical sensors, etc.

As will be appreciated from the following description of the method 300,the method may involve continually taking images of a region of apatient's body at times determined by motion estimation data such as amotion vector, and then continuously updating the motion estimation data(e.g. motion vector) based on the newly acquired images to ensure thedetermined motion of the region is kept up to date. This can bedescribed as tracking movement of the region in real time. If subsequentimages suggest that the range of motion data is inaccurate or else nolonger representative of the movement of the patient, the range ofmotion data may be automatically updated.

In an implementation, the position of the region is determined in eachsubsequent image at block 302 and compared to the range of motion data.If this determination reveals that the region has extended beyond theminimum or maximum displacements indicated by the range of motion data,then the range of motion data can be updated, for example to comprise anew maximum or minimum displacement as appropriate. Alternatively, therange of motion data may be updated only if the region is observed toregularly travel beyond the range of motion data. This may involvecomparing to a threshold value, which may be a number of times theregion has extended beyond the range of motion determined at 305, orelse a number per unit time. Thus. movement of the region is tracked,the determined motion of the target is compared to the range of motiondata to determine whether the range of motion data is still appropriateand accurate.

At block 306, a motion event time is predicted. The motion event time ispredicted based on the determined motion of the region. The motion eventtime is a time at which a motion event will occur. A motion event occurswhen at least one property associated with the motion and/or location ofthe region meets a criterion, or meets a combination of criteria. Forexample, a motion event may occur when a property of the motion changesin a specified way. For example, the velocity might change such that avelocity criterion is met. The criterion could relate to either or both,of the speed or direction of movement.

Motion events may occur when the region reaches particular points in itsrange of motion, for example the points of maximum and its minimumdisplacement in the patient's respiration cycle. The maximum and minimumdisplacement points may be referred to as turning points and can bedetermined, for example, from the range of motion data. For regionswhich move as the patient breathes, these maximum and minimumdisplacements may correspond in time roughly, though not necessarilyexactly, with the maximum and minimum points of lung expansion. Forperiodic movements which generally involve two main directions ofmovement, for example a positive and a negative direction of movement,these maximum and minimum points of movement may be referred to asturning points.

When predicting when the next turning point or other motion event willoccur, the range of motion data may be used. In a simple implementation,the relevant event time to be determined might be the time at which theregion is at its maximum displacement. This corresponds at least roughlywith the point of maximum inhalation, i.e. when the lungs are at theirgreatest extension point. Because the velocity of the region at aparticular time is known from motion estimation techniques, and becausethe distance to be travelled can be determined from the range of motiondata, an estimate of when the region will reach its maximum displacementcan be extracted.

FIG. 4 a depicts an image acquisition scheme in which the motion eventsof interest are particular points in a range of motion associated withthe region. In particular, the motion events of interest are the turningpoints of the motion. A suitable criterion might be that the region hasa displacement which matches either its maximum or minimum displacementvalue, as determined from the range of motion data. In this example,using the determined motion, e.g. a motion vector, and the range ofmotion data, an event time t_(event(1)) be determined at which theregion will arrive at its minimum displacement.

Three different predicted motion event times are depicted on the graphof FIG. 4 . These three different predicted motion event times aremarked using the notation: t_(event(identifying number)).

An alternative criterion for predicting when the region is at a turningpoint might be that the velocity changes from positive to negative (forexample in a co-ordinate system in which zero displacement is at theregion's minimum value). A suitable criterion can be described as thedirection of motion having changed in a specified way, e.g. by athreshold amount, or the direction being within a specified directionrange. For example, the direction of the region can be placed in one oftwo bins: up or down, or equivalently positive or negative. Predicting amotion event time might therefore comprise predicting when the directionof the region will change from one ‘bin’ or range of direction values toanother.

In order to predict when a change in velocity will occur, it is usefulto have determined, at 303, a plurality of different motion vectors atdifferent times. For example, in an implementation, the plurality ofimages 402 a received at block 301 may comprise three images, each takenat a respective image capture time. By determining a first motion vectorbased on the difference in position of the region between a first andsecond image, and determining a second motion vector based on thedifference in position of the region between the second and a thirdimage, it is possible to determine acceleration information for theregion. Accordingly, it is then possible to predict a future motionevent time at which the velocity of the region will meet certaincriteria, for example when the velocity will change direction at aturning point. Techniques utilising Kalman filters can also be used todetermine acceleration information. These techniques are known to theskilled person.

FIG. 4 b shows a different image acquisition scheme in which therelevant motion event is the region of the patient entering or exiting aspecified region 412 of the patient. The specified region may be athree-dimensional volume within the treatment volume of a radiotherapydevice. The region of the patient which is being tracked, for example atumour or other target region, moves with respect to the specifiedregion, for example as the patient breathes. The size of the specifiedregion is depicted in FIG. 4 b using the numeral 412. To determine whento take subsequent images, a motion vector 410 b is determined based ona plurality of images 402 b. Based on the motion vector 410 b it ispossible to predict a time t_(event(1)) at which the target region ofthe patient will exit the specified region 412. While reference is madeto the target region exiting the specified region, it can beequivalently predicted at what time the target region will enter thespecified region. A predicted motion event time at which the targetregion of the patient will enter the specified region is shown att_(event(2)).

Determining when a target region such as a tumour is in a specifiedregion or volume with high accuracy is important when determining whento gate a radiotherapy treatment beam. The image acquisition scheme andmethod described with respect to FIG. 4 b is useful for the purposes ofradiotherapy because, typically, it is advantageous to apply thetreatment beam while the patient is at the mid-point of theirrespiration cycle; in other words, when the target region is roughlymidway between the minimum and maximum point of its range of respiratorymotion. A radiotherapy device may be configured to direct the treatmentbeam toward the specified region or volume, with the patient beingpositioned before the treatment such that the tumour or target regionaligns with this location. In some implementations, the isocentre of theradiotherapy device may fall within the specified region. The treatmentbeam can then be gated, i.e. switched off, when the tumour moves outsidethis specified region/volume by a particular degree. In this way, theradiation dose received by healthy tissue is reduced.

For this reason, a suitable time to take a subsequent image might be,depending on the nature of treatment, the mid-point of the respirationcycle. At this point, the displacement, or position, of the targetregion will meet the criterion that it is located at the midpointbetween minimum and maximum displacement in the range of respiratorymotion. The criterion might similarly be that the target region fallswithin a threshold range or specified region located around themid-point of the respiratory cycle. In other words, a propertyassociated with the motion of the region (in this case, the position ofthe region) will meet the criterion of being located at a specifiedposition value (the mid-point), or alternatively the location of thetarget region will meet the criterion of being within a specifiedregion, where the specified region includes the mid-point.

Based on the above discussion it will be appreciated that, based on thedetermined motion obtained from motion estimation techniques, it ispossible to predict a motion event time at which at least one propertyassociated with the motion will meet at least one criterion. Thecriterion may be, for example, a velocity criterion, a displacement orlocation criterion, or an acceleration criterion if multiple motionvectors are acquired from the plurality of images received at block 301.

At block 308, at least one subsequent image capture time is determined.The subsequent image capture time is determined based on the predictedmotion event time. The subsequent image capture time is a time at whicha subsequent image should be captured. The method may further comprisesequencing the subsequent image(s), e.g. by instructing the imagingapparatus of the medical device to obtain a subsequent image at thesubsequent image capture time. At least one subsequent image may then beobtained at the at least one subsequent image capture time (see block310).

For example, the subsequent image capture time might correspond with thepredicted motion event time. In an implementation in which the motionevents relate to the turning points of the movement, images could thusbe obtained at the turning points. Alternatively or additionally, amotion event may be the region of the patient being located at apre-specified location, such as a location corresponding with themid-point of the patient's respiratory cycle. Images can thus bescheduled to be taken at the mid-point of the patient's respiratorycycle.

In another implementation, a subsequent image capture time can bedetermined to be a specified time before and/or after the predictedmotion event time. Such an implementation is depicted in FIG. 4 a .Based on motion vector 410 a, a turning point is predicted to occur att_(event(1)). Based on this predicted motion event time t_(event(1)),two subsequent image capture times are determined: the first subsequentimage capture time being a first specified time interval 432 beforet_(event(1)), and the second subsequent image capture time being asecond specified time interval 434 after t_(event(1)).

The specified time intervals 432, 434 may be based on predeterminedperiodicity data. For example, the periodicity or length of thepatient's breathing cycle can be obtained from the second plurality ofimages 404, and the specified time interval(s) 432, 434 may bedetermined relative to this length of time. In an example, the firstspecified time interval 432 might be a small fraction of the patient'stotal breathing cycle length. In a particular example, the specifiedtime interval might be 5% of the time taken for the patient to completea full respiratory cycle, such that if a patient's breathing cycle ismeasured to last 6.0 seconds, a subsequent image is scheduled to occur0.3 seconds before a predicted motion event takes place.

In an alternative implementation, rather than taking images at aspecified time interval before a predicted motion event time, asubsequent image could be scheduled for a time at which the region ofthe patient will meet a distance or displacement criterion. This may bedescribed as a dynamic threshold, as it will be adjusted depending onthe speed/velocity of the region. With reference to FIG. 4 a ,t_(event(1)) is a predicted motion event time. At this time, it ispredicted that a turning point will occur. A subsequent image 422 couldbe scheduled when the region is expected to be a specified distance awayfrom the turning point. With reference to FIG. 4 a , the time difference432 between the predicted event time t_(event(1)) and the time at whichthe subsequent image 422 is taken could be based on a position ordisplacement criterion. Put simply: this implementation involvespredicting at what time the target region will be within a thresholddistance of the turning point, and scheduling a subsequent image to betaken at that time.

It will be appreciated that, having predicted a motion event time, theat least one subsequent image capture time may be determined in one way,or a combination of different ways. Scheduling subsequent images to betaken at either a threshold time or a threshold distance before aturning point has been described, but different criteria and events maybe used.

Thus, it is possible to determine multiple different subsequent imagecapture times, and the resulting images can serve different purposes.For example, a first image taken at or just before the motion eventoccurs can be used to obtain an image at a time which will be mostuseful for updating radiotherapy treatment. A second image can bescheduled to occur just after the motion event at a time mostappropriate for updating the determined motion of the target, i.e. at atime which will result in an image most suited for the determination ofa new motion vector. Times 432 and 434 can be selected and adjustedbased on these aims.

After subsequent image(s) have been obtained at block 310, the locationsof the region in each subsequent image is determined at block 302, andthe process continues through the steps 302 to 310. In this way, themotion vector is continually updated and kept up to date. Inimplementations in which the region is being tracked for the purpose ofadapting radiotherapy treatment, this method ensures that the treatmentis optimised to take into account as intrafractional motions whilerequiring significantly fewer images to be taken.

As described in relation to block 304, the tracking method can be usedto update radiotherapy treatment, in real-time or semi-real time. Thetreatment is kept up to date by updating the radiotherapy based on thesubsequent images captured at the subsequent image capture times. Byadjusting the radiotherapy treatment in this way, radiation dosereceived at the target region can be optimised while minimisingradiation dose received by healthy tissue, and in particular minimisingradiation dose received by any OARs.

Updating the radiotherapy treatment may comprise adjusting one or moreparameters which affect the delivery of radiation to the patient.Updating the treatment may comprise halting (gating) application of theradiotherapy beam based on the subsequent image, and/or may compriseadjusting the shape and/or intensity of the beam. The shape of the beammay be adjusted using a multi-leaf collimator. Accordingly, thepositions of the leaves of the MLC may be adjusted according to thesubsequent image. Updating the radiotherapy treatment may compriseadjusting the relative positions of the patient and the source oftherapeutic radiation, for example by moving the patient positioningsurface, and/or by rotating the gantry such that the therapeuticradiation beam is emitted at a different angle with respect to thepatient.

The controller instructs the relevant parts of the radiotherapy deviceto effect the updated radiotherapy treatment. For example, thecontroller is configured to send control signals to any of the radiationsource and its associated control circuitry, collimation components suchas the MLC, and components which can effect changes in relative positionbetween the patient and the radiation source such as the patientpositioning surface and the gantry rotation mechanism.

As an example, as part of radiotherapy treatment, a source oftherapeutic radiation is configured to apply radiation along a beam pathwithin the treatment volume of the radiotherapy device from a particulargantry rotation angle for a specified length of time, in order to supplya particular dose of radiation to a tumour of a patient positioned onthe patient positioning surface. Before treatment, the patient ispositioned on the patient positioning surface such that the tumour ispositioned in the beam path. The MLC is used to shape the radiation suchthat the dose applied to the tumour or target region is optimised whiledose applied to healthy tissue is minimised. In other words, theradiotherapy device is configured to apply radiation to a specifiedvolume of space. However, as described herein, the tumour moves with thepatient's breathing, and so the tumour does not occupy the same volumeas the specified volume at all times. The present methods can be used totrack the tumour and gate (halt) the application of radiotherapy whenthe tumour leaves the specified volume by a specified amount, or elsetack the tumour as the patient breathes by adjusting the MLC leaves.

The present methods can be used to predict when the target region isgoing to enter, and exit, a predefined volume, and take images of thetarget region based on these predicted times. Such an implementation canbe described with respect to FIG. 4 b . FIG. 4 b depicts a specifiedregion 412, which may be a volume. The patient is aligned such that thetumour aligns with the specified region 412. The application ofradiation should be halted when the target region exits the specifiedregion 412 by a specified degree in order to reduce radiation dose tohealthy tissue. The application of radiation should begin again when thetarget region re-enters the specified region 412 by a specified degreein order to optimise dose applied to the tumour. The determined motionof the tumour, as characterised by motion vectors, can be used topredict when these two events will happen. Knowledge of when theseevents will happen, or else are likely to happen, allows the tracking ofthe tumour with sufficient accuracy to allow the beam to be gatedsuccessfully, while minimising the number of images required.

The motion vector 410 b might suggest that the patient has started tobreathe more quickly. A time t_(event(1)) at which the target regionwill leave the specified region is predicted, and a subsequent image istaken just before this predicted time t_(event(1)) (controlled byspecified time interval 434 b) to confirm that the target region isexiting, or just about to exit, the specified region. Based on thissubsequent image, which confirms that the target region is about to exitthe specified region, the application of radiation to the specifiedregion 412 is halted.

Based on the subsequent image, plus any other images taken (not shown inFIG. 4 b ), it can also be predicted when the target region willre-enter the specified region. This predicted motion event at timet_(event(2)) is depicted in FIG. 4 b . Again, an image is taken justbefore the predicted motion event time t_(event(2)) to confirm themotion estimate tracking is correct. Based on this image, theradiotherapy treatment is updated such that radiation is re-applied tothe specified region 412/volume of space.

To track the region in real time or semi-real time, the determinedmotion is continually updated. With reference to FIG. 3 , after at leastone subsequent image has been obtained at 310, the at least onesubsequent image is used to update the determined motion and the methodreturns to block 303. At block 303, a new motion vector is determinedbased at least in part on the at least one subsequent image acquired at310, and a new motion event time is predicted at block 306 based on theupdated motion vector.

In this way, the motion of the region is tracked in real time using anoptimised number of images. Thus, when the disclosed methods are usedwith an imaging apparatus comprises a source of ionising imagingradiation, a dose received by the patient is kept as low as possible fora given accuracy of motion tracking.

In continuously updating the motion vector (determined motion), timesfor a plurality of motion events are predicted and a plurality ofsubsequent images can be sequenced based on these times. The presentmethods may thus comprise receiving a first subsequent image, updatingthe determined motion of the region based at least in part on the firstsubsequent image and a first subsequent image capture time; predicting,based on the updated motion of the region, a second motion event time atwhich the at least one property associated with the motion will meet theat least one criterion; and determining, based on the second predictedmotion event time, a second subsequent image capture time at which asecond subsequent image should be captured.

The present methods are advantageous for several reasons. By determininga motion of the region and basing subsequent image capture times onpredicted event times, rather than constantly taking images as has beendone previously, optimal use of the number of images is made. This isbeneficial in situations in which there is a ‘cost’ to taking images,for example when the images are taken by an imaging apparatus whichmakes use of ionising radiation. In this scenario, the cost may be aradiation dose applied to a patient, or else the radiation damagereceived by electronics when taking an image—this latter point isrelevant in implementations using the method to determine when to takeX-ray images in an airport security setting, for example.

To date, it has been thought that the use of kV images is not suitableto track a tumour or other target region of a patient in real time. Ithas been thought that the required frame rate and consequent radiationdose to the patient would be too high. However, by making use of motionestimation techniques to control when images are captured, this dose canbe reduced to acceptable levels while maintaining both tracking accuracyand a high response time to changes in the motion of the tumour or othertracked region. Reducing the number of images taken by the kV images notonly reduces the dose supplied to the patient, but also prolongs thelifetime of the imaging radiation detector.

Methods of the present disclosure have a fast response time to changesin the motion of the tumour. By predicting future event times based on acontinuously updated motion vector which describes motion of the region,image sequencing times can be automatically updated as the patient'sbreathing speed changes. In turn, this allows radiotherapy treatment tobe updated in real time to improve the efficacy of the treatment, bytracking the tumour as it moves or by gating application of thetreatment beam, all with minimal kV imaging dose.

By imaging the tumour and basing subsequent image sequencing times onthese images using motion estimation techniques, it is possible todirectly track motion of the tumour or other region of the patient'sbody. This is more effective and more accurate than techniques which,for example, make use of a surrogate signal for the patient's breathingand rely on a potentially inaccurate correspondence mapping between thesurrogate signal and the tumour movement.

FIG. 5 depicts a control diagram showing an implementation of thepresent disclosure. The process starts at 510. Therapeutic radiation isdelivered to the patient at box 522. The treatment beam, e.g. the MVtreatment radiation, is enabled. This involves control signals beingsent from a controller to the source of radiation/beam generationsystem. At box 524, the radiotherapy treatment is updated. Inparticular, one or more parameters which affect the delivery ofradiation to the patient may be adjusted, for example the treatment beammay be adjusted (e.g. in its intensity), gated (i.e. stopped orstarted), or moved (e.g. by virtue of steering magnets or via beamshaping/collimating apparatus such as an MLC). The adjustments orupdates made at box 524 are performed based on information from imagingcontrol loop 530 which will be discussed below. At box 526, it isdetermined whether treatment has completed or not. If so, the treatmentis ended at box 528. If not, the treatment beam is enabled again andtreatment continues at box 522 based on the parameters which have beenadjusted or updated at box 524.

The manner in which treatment should be updated is informed by animaging control loop 530, indicated in FIG. 5 via a dashed line. At step531, control signals are sent from the controller to the imaging systemin order to enable the imaging radiation. Images are acquired via theimaging system at box 532. The resulting image is analysed at box 533 todetermine the position of the tumour, or other target region or regionof interest. Based on the image, or images, acquired at box 532, themotion of the tumour is determined at box 534 in the form of a motionvector. Based on the determined motion vector, a future event timeassociated with the tumour is determined at box 535. As discussedelsewhere herein, this event time might be a time at which the locationof the tumour meets a criterion, for example when the tumour will belocated within the beam path of the treatment beam or when the tumour isat a turning point associated with the patient's respiratory cycle.

At box 526, future kV images are sequenced based on the event timecalculated at box 535. As discussed elsewhere herein, the images may besequenced to occur at the calculated future event time, or else may besequenced to occur just before or after the future event time. Thisdepends on the requirements of the radiotherapy treatment and the typeof motion event predicted at box 535. Control signals are sent form thecontroller to the imaging system at box 531 to enable kV radiation andimages are acquired at 532 at the sequenced times.

Optimising the number of images required in order to maintain a giventracking accuracy can be achieved using known optimisation techniques. Asuitable loss or cost function can be prepared with the general aim oftaking the minimum number of images for a given motion detectionaccuracy. The relationship between tracking accuracy and the number ofimages taken can be explored by using a phantom in the imaging/treatmentvolume of the radiotherapy device. The phantom is a moving phantom,which has a position which changes with time in a known way. By takingimages of moving phantoms, it is possible to calibrate the method andexplore the relationship between number of images taken and trackingaccuracy. The resulting calibration information is used to inform theoptimisation process.

Known methods of motion estimation may be used as part of the methods ofthe present application. Existing methods of motion estimation can beused to analyse the images received at block 301 of method 300.Algorithms can be used to create one or more motion vectors thatrepresent the 2D translational motion of image areas from one frame tothe next. Techniques may involve a motion search, in which two imagesare searched to find areas of the image that best match the region ofthe patient. The difference in the position of the two areas allows adetermination of a motion vector which describes motion of the regionbetween the relevant image capture times.

FIGS. 6 a and 6 b depict a plurality of images 610, 620 from which amotion vector may be determined. The first image 610 is taken at a firstimage capture time and depicts an object at a first location 612. Thesecond image 620 is taken at a second image capture time and shows thesame object at a second location 622. The displacement of the object canbe determined, by analysing the first and second image 610, 620 usingmotion estimation techniques. In the example shown, the difference intwo dimensions between the original position 612 and the subsequentposition 622 can be described by a 2D vector as follows: (3, −3). Amotion vector comprising velocity and direction information can becalculated using this displacement vector and the first and second imagecapture times.

In an implementation, Kalman filters may be used to determine the motionof a tumour or other region of the patient's body. A Kalman filter is adata fitting methodology which may be used to predict a tumour's futurestate, such as its future location and velocity, based on past statemeasurements. Kalman filters have been used in other areas for trackingmoving objects in video and the skilled person will be familiar withsuch techniques. Advantages of using a Kalman filter include that thetechnique is light on memory and is fast and so can be used forreal-time tracking.

Radiotherapy treatment typically involves rotating the gantry of theradiotherapy device during treatment such that the source of therapeuticradiation is rotated around the patient. This not only changes the anglefrom which the treatment beam is directed at the patient, but also theangle at which images can be taken. This means that, during radiotherapytreatment, it may be necessary to compensate for the gantry as arotating frame of reference. As the gantry is rotated by differentdegrees to different gantry rotation angles (see FIGS. 1 b-1 e ),everything in the view of the imaging apparatus is rotated by a setamount. Known motion estimation techniques can be used to compensate forthis rotation.

Therefore, methods of the present disclosure may comprise determiningmotion of the region while the imaging apparatus is at a particulargantry rotation angle, predicting a motion event time and determining atleast one subsequent image capture time, and then updating thedetermined motion based on images taken at a second, different gantryrotation angle.

In an example, the process makes use of a reference feature in theimages in order to account for rotation of the gantry. For example, theaverage position or centre of mass of the patient could be used as areference point. These features do not change position significantlybetween frames. Other possible reference features include an object inthe patient that will not move with patient respiration, e.g. a suitablebone or other visible feature which will remain sufficiently stationarythroughout the treatment. Other example reference features include theedge of the table/couch, or else markers placed in or on the table forthe purpose of providing reference points.

Accounting for gantry rotation may alternatively or additionallycomprise making use of information from the controller regarding thegantry rotation angle. For example, each image may be tagged with notonly the time at which it was taken, but also the angle from which itwas taken. This information can be fed into known motion estimationtechniques in order to better track the tumour or other target region.

Accounting for gantry rotation may alternatively or additionallycomprise developing and using a computer model of the patient's motion.This model can be used to calculate the expected change in patientprojection for a given gantry angle.

The above implementations have been described by way of example only,and the described implementations and arrangements are to be consideredin all respects only as illustrative and not restrictive. It will beappreciated that variations of the described implementations andarrangements may be made without departing from the scope of theinvention.

At blocks 301 and 701, reference is made to receiving one or more imagesof a patient's body. While the discussion herein has focused onreceiving at least two images and comparing the movement of the regionbetween the two frames, it is possible to determine motion, including todetermine a motion vector, using a single image, for example byanalysing the degree of blur associated with the moving region. Thesetechniques are known and do not need to be discussed herein.

While reference is made primarily to accounting for changes in positionof a tumour as a patient breathes, the method is not limited to trackingeither a tumour or to tracking motions associated with respiration.There are many other uses of the disclosed methods. When tracking atumour or target region, the goal is to ensure the prescribed dose isapplied to that region while dose to the surrounding regions isminimised. However, equivalently, an organ at risk (OAR) may be trackedusing the present techniques. By tracking an OAR and by setting up themotion event and image sequencing criteria appropriately, it is possibleto ensure dose applied to the OAR is minimised during treatment.Ensuring a low dose is provided to OARs is particularly important whentracking cardiac movements associated with the beating of a patient'sheart, or when accounting for the movement of tissues near the throat asa patient moves their mouth and throat, for example if they swallowduring treatment. In these sensitive areas, radiation therapy needs tobe highly accurate, and reducing both treatment and imaging dose isparticularly important.

Methods of the present disclosure can be used to track multiple regions,including both OARs and target regions. FIG. 7 depicts a disclosedmethod 700 of tracking a plurality of regions of a patient's body. Themethod 700 is similar to the method 300 depicted in FIG. 3 and likenumerals are used to indicate like steps. Reference is made to a firstand a second region. The first region may be a tumour or target region,for example, and the second region may be an OAR. It will be appreciatedthat the method is not limited to just two regions and a plurality ofregions may be tracked using the disclosed methods.

At 701, one or more images of a patient's body are received. The image,or images, are analysed in order to determine the location of the firstregion in the image(s) at box 702 a and the location of the secondregion in the image(s) at box 702 b. A motion of the first region of thepatient's body is determined at box 703 a. This may comprise determininga first motion vector associated with the first region. The image orimages are similarly analysed at box 703 b to determine the motion of asecond region, for example to determine a second motion vectorassociated with the second region.

Radiotherapy treatment may be updated at box 704 based on any of: thedetermined location of the first region, the determined location of thesecond region, the determined motion of the first region, and thedetermined motion of the second region.

Motion event times are predicted for both the first and second region atboxes 706 a and 706 b. Though not shown in FIG. 7 , the method 700 mayfurther comprise determining and using first range of motion informationfor the first region and second range of motion information for thesecond region in the manner described above in relation to FIG. 3 .

As described above, a motion event occurs when at least one propertyassociated with the motion meets a criterion, or meets differentcriteria. The criteria for the first and second region may be the same,or may be different. For example, if the treatment beam is positioned toirradiate a particular region or volume of the patient, the relevantmotion events being tracked may be the entering and exiting of eitherthe first or the second region into this volume of the patient. Theevents may also be associated with the turning points of the patient'srespiratory cycle, as described elsewhere herein.

At box 708, one or more subsequent image capture times are determined.The image capture times are determined, and sequenced, based on thefirst and second predicted motion event times. For example, images maybe sequenced just before either of the regions are expected, based ontheir motion vectors, to enter or exit a volume of the patient beingirradiated. Images taken at these times allow the radiotherapy treatmentto be updated accurately at step 712. Suitable criteria can beestablished such that treatment radiation is applied to the patient onlywhile the tumour is sufficiently located in the volume of the patientbeing irradiated, and such that if the OAR enters the volume to aspecified degree the beam is gated.

For example, a motion event may occur when a property of the motionchanges in a specified way. For example, the velocity might change suchthat a velocity criterion is met. The criterion could relate to eitheror both, of the speed or direction of movement.

Finally, images are obtained at the determined subsequent image capturetimes, and the process returns to bocks 702 a and 702 b at which thelocations of the first and second region are determined in thesubsequent images. As with FIG. 3 , the process is continued to ensurethe motion vectors or other parameters associated with motion are keptup to date.

While reference has been made to obtaining images using a kV imagingsystem such as a CT imaging apparatus, the method is not limited to usewith such hardware. For example, in a radiotherapy environment,so-called ‘portal’ images obtained via an electronic portal imagingdevice (EPID) may be used. These images may be used as an alternative,or in addition to using kV images.

In an example implementation, a plurality of images are received atblock 301 of method 300, where one or more of the images are kV imagesobtained via a kV imaging device and one or more of the images areportal images obtained via an EPID. It is possible to determine thelocation of the region of interest in each of these images and use thesepositions to allow determination of the motion vector.

This implementation is particularly useful for radiotherapy devicescomprising both kV imaging systems and EPIDs. The location of the regionas located in images taken by these different imaging modalities can beconverted into a common reference frame. For example, the location ofthe region can be described using the room-coordinates. By making use ofimages taken by different imaging modalities, and/or different imagingsystems oriented at different gantry angles of the radiotherapy device,more accurate motion vectors can be determined.

Validating a Patient Movement Model

In a disclosed implementation, a surrogate signal may be used to trackinternal movement of the region of the patient (e.g. a tumour). Thesurrogate signal may be, for example, a signal from a resistance bandaround a patient's chest, or the output from a surface scanning system(for example an optical surface scanning system). The surrogate signalis not directly representative of the movement of the region of interestinside the patient, however it is possible to generate a patientmovement model which links, or maps, the surrogate signal to thisinternal movement.

In an example, a surface scanning system is used to monitor patientmovement while the patient is positioned on the patient positioningsurface of the radiotherapy device. The monitored movement includes thepatient's respiratory motion. The output of the surface scanning systemis representative of the movement of the patient's surface or ‘external’anatomy. During this time, kV images (e.g. X-ray and/or CT images) or MRimages are taken which show the region of interest. In contrast with thesurface scanning system output, these images show internal patientanatomy. By continuously monitoring the patient using the surfacetracking system while kV images are taken, it is possible to generate apatient movement model which links the movement of the patient's surfaceanatomy with the movement of the patient's internal anatomy. Inparticular, the movement of the patient's surface anatomy, as determinedby the surface scanning system, can be used as a surrogate signal todetermine the movement of the internal region of interest.

Once the patient movement model has been generated, it is no longernecessary to take kV images in order to determine, or predict, thelocation of the region of interest inside the patient. The surrogatesignal (e.g. output of the surface scanning system) can be used todetermine the location of the region of interest, either at a currenttime or at a future time. The surrogate signal may be used todetermine/predict when a tumour will be positioned in the path of aradiotherapy beam. As such, the surrogate signal may be used toadapt/update/modify radiotherapy treatment in real time. The signal maybe used to update radiotherapy treatment, for example by adjusting oneor more parameters which affect the delivery of radiation to thepatient, in any of the ways described elsewhere herein. By building apatient movement model in this way, the internal motion of the tumourcan be tracked using the surrogate signal without having to continuallytake kV images. Hence, the dose delivered to the patient can bebeneficially reduced.

However, for optimal results, the accuracy of the patient movement modelshould be validated throughout treatment to ensure good agreementbetween the expected position of the tumour or other region of interest(as predicted by the patient movement model) and the actual position ofthe tumour (which may be determined by taking a kV or MRI image such asa projection image). Accordingly, methods of the present disclosure canbe used to determine the optimal time to take a kV image in order tovalidate a patient movement model.

Once a motion of the region has been determined (e.g. once a motionvector associated with the tumour has been determined) based on one ormore initial kV images, that motion vector can be used as the basis forupdating the patient movement model. In an example, a motion event timeis determined. At this motion event time, it is predicted that aproperty associated with the motion or position of the region will meeta criterion (e.g. a time at which the tumour is at a turning point ofits respiratory motion). This determination may be determined based onkV images, as described elsewhere herein. A subsequent kV image can thenbe scheduled based on knowledge of the predicted motion event time, forexample at that time or at a time close to the motion event time. Oncethe subsequent image is taken, the position of the tumour in thesubsequent image can be compared with the position of the tumour aspredicted by the patient movement model. The patient movement model canthen be updated, if needed, based on the level of agreement between thepredicted position (determined by the model) and the actual position atthe (determined by the kV image). The model can be updated based on thedifference between expected and actual position of the tumour at thetime the subsequent kV image was taken, as well as the output from thesurface scanning system at that time. In contrast, if the positionsagree within a threshold degree of tolerance, then the patient movementmodel is validated, and no updates are required.

It has been found that patient movement models based on surrogatesignals tend to predict the movement of a tumour very accurately as thetumour moves between the maximum and minimum of its range of motion (forexample between the maximum and minimum of the patient's respiratorycycle). However, the timing and duration of the turning points in thecycle can vary with time, and th accuracy of the model will reduce withtime unless it is updated. By taking kV images at or around theseturning points, kV images for validating the model are taken at the timewhen the greatest benefit can be provided to the validation process. Inother words, by determining one or more subsequent image capture timesbased on when the region of interest will be positioned, for example, ata particular point in its range of motion, subsequent images can bescheduled for times when the greatest benefit to the validation processcan be achieved, while still keeping the overall dosage delivered to thepatient to a minimum.

The tracking of a region of a patient using a patient movement model,and the validation of the model, can be described in relation to FIG. 3. At 301, one or more images of a region of the patient's body arereceived. In an implementation, a plurality of images is received whichare taken throughout the range of motion of the region, e.g. throughoutthe patient's respiratory cycle. These images are taken with an imagingsystem comprising a source of imaging radiation. A surrogate signal isalso received (not shown in the flowchart) which is indicative of themovement of the patient's external/surface anatomy. The surrogate signalhas been acquired during the same time period as the plurality of imageswere taken. At 302, the location of the region in the images isdetermined, for example using auto-segmentation techniques. At block303, the motion of the region is determined. This may comprisegenerating a patient movement model based on the one or more initialimages and the surrogate signal. The patient movement model enablesestimation of the motion or position of the region, or else some otherproperty associated with the motion of position of the region, as afunction of the surrogate signal and/or as a function of time. In otherwords, using the patient movement model, it is possible to estimate themotion or position of the region at a later time.

Now the patient movement model has been generated, it is possible tomonitor the surrogate signal and determine the motion or position of theregion either in real-time, or in the near future, with high accuracy.Accordingly, the surrogate signal may be used as the basis for updatinga radiotherapy treatment plan at block 304. The surrogate signal may beused as a trigger to gate the beam, adjust the beam shape, or update thetreatment in any other way described herein.

At block 306, a motion event time is predicted using the determinedmotion, and in particular using the patient movement model. One of theaims of this implementation of the method is to check the accuracy andvalidity of the patient movement model, and the times where the model islikely to be least accurate are at the turning points of the patientrespiratory cycle. Accordingly, in an example, the predicted motionevent time may be a time at which, according to the patient movementmodel, the region will be positioned at a turning point in the patient'srespiratory cycle. This may be at a maximum or minimum of the region'srange of motion.

At block 308, one or more subsequent image capture times are determined.These image capture times are determined based on the motion event timepredicted at block 306. For example, it may be determined that asubsequent image should be captured at, or just before, the turningpoint in the patient's respiratory cycle. At block 310, subsequentimages are obtained at the image capture times determined at block 308.

The method then returns to block 302, at which the location, position,or other property associated with the motion or position of the regionis determined in the subsequent image or images. Again, this may beachieved using auto-segmentation or contouring techniques or otherstandard image analysis techniques. This determined position allows thepatient movement model to be validated and updated, if necessary. Tovalidate the model, the method further comprises using the patientmovement model to estimate the location (or else another propertyassociated with the motion or position of the region) at the subsequentimage capture times. Comparing the estimated position (determined usingthe patient movement model) with the determined or measured position(determined using the subsequent images) allows a difference between thelocations to be determined, and the model can be updated based on thisdifference.

If the difference, i.e. the level of agreement between the model and themeasured result, is large, then a frequency of kV images to be scheduledcan be increased in order to provide more data with which to update themodel. If the difference is small, i.e. if the model is accurate, then afrequency of future kV images may be reduced. In this way, the patientmovement model is kept accurate while minimising the number of kV imagesrequired, hence reducing overall radiation dose to the patient. If thesurrogate signal, e.g. the surface tracking signal, is used to guideradiation therapy, then the treatment can be optimised using thesurrogate signal without subjecting the patient to a high dose ofimaging radiation.

While reference is made to radiation therapy, the present methods oftracking a region have several different uses. It is useful to analysehow regions of the patient's body move in real-time or semi-real timefor diagnostic reasons. The methods may also have applications outsidethe field of medicine. For example, disclosed methods may have securityapplications, for example when determining when to take images as aperson walks through a scanner, or when taking images of the content ofluggage at an airport using penetrating waves.

The above implementations have been described by way of example only,and the described implementations and arrangements are to be consideredin all respects only as illustrative and not restrictive. It will beappreciated that variations of the described implementations andarrangements may be made without departing from the scope of theinvention.

1. A medical device for tracking movement of a region of a body of apatient, the region of the body of the patient having a range of motion,the medical device comprising: a controller, the controller comprisingcontroller circuitry configured to: determine a motion of the regionbased on one or more initial images depicting at least part of theregion; predict, based on the determined motion, a motion event time atwhich at least one property associated with the motion or a position ofthe region will meet at least one criterion, wherein the at least onecriterion comprises the region being at a particular location in itsrange of motion; and determine, based on the predicted motion eventtime, at least one subsequent image capture time at which at least onesubsequent image should be captured.
 2. The medical device of claim 1,further comprising: an imaging apparatus, wherein the one or moreinitial images are obtained using the imaging apparatus.
 3. The medicaldevice of claim 2, wherein the imaging apparatus comprises: a source ofimaging radiation.
 4. The medical device of claim 2, the controllercircuitry being configured to: send one or more instructions to theimaging apparatus to obtain the at least one subsequent image at thedetermined at least one subsequent image capture time.
 5. The medicaldevice of claim 1, the controller circuitry being further configured to:determine the location of the region in the one or more initial images;and determine the motion of the region based on the determined locationof the region in the one or more initial images and image capture timesassociated with the one or more initial images.
 6. The medical device ofclaim 1, wherein the at least one property associated with the motion orposition of the region comprises one or more of: a direction of motion;or a speed of motion; wherein predicting a motion event time comprisespredicting when the at least one property will change by a thresholdamount.
 7. (canceled)
 8. The medical device of claim 1, wherein themedical device is a radiotherapy device comprising a source oftherapeutic radiation, and the controller circuitry is furtherconfigured to: update a radiotherapy treatment based on at least one ofthe determined motion or a location of the region in the subsequentimages, wherein updating the radiotherapy treatment comprises adjustingone or more parameters that affects delivery of radiation to thepatient.
 9. (canceled)
 10. The medical device of claim 8, the controllercircuitry being configured to: control at least one of the followingbased on at least one of the determined motion of the region or thelocation of the region in the at least one subsequent image: a gating ofa radiation beam; a shape of the beam via adjusting beam shapingapparatus; a direction of the beam; and a movement of a patientsupporting system.
 11. The medical device of claim 1, wherein at leastone of: the at least one property includes the position of the region,and the criterion comprises the region entering, or overlapping by athreshold amount, a predefined region within a treatment volume of themedical device; or determining the motion of the region comprisesdetermining a motion vector for the region.
 12. (canceled)
 13. Themedical device of claim 1, the controller circuitry being furtherconfigured to at least one of: receive the at least one subsequentimage; determine an updated motion of the region based on the at leastone subsequent image; predict, based on the updated motion, a secondmotion event time at which at least one property associated with themotion or position of the region will meet the at least one criterion;or determine, based on the predicted second motion event time, at leastone further subsequent image capture time at which a further subsequentimage should be captured.
 14. (canceled)
 15. The medical device of claim8, wherein determining the motion of the region comprises: generating apatient movement model based on the one or more initial images and asurrogate signal, the surrogate signal being indicative of movement of asurface anatomy of the patient, and wherein the patient movement modelenables estimation of the at least one property associated with themotion or position of the region as a function of the surrogate signaland/or as a function of time.
 16. (canceled)
 17. The medical device ofclaim 15, wherein predicting the motion event time based on thedetermined motion comprises predicting the motion event time using thepatient movement model.
 18. The medical device of claim 15, wherein thecontroller circuitry is further configured to: estimate, using thepatient movement model, the at least one property associated with themotion or position of the region at the at least one subsequent imagecapture time; receive the at least one subsequent image; determine theat least one property associated with the motion or position of theregion at the at least one subsequent image capture time using the atleast one subsequent image; and update the patient movement model basedon a difference between the estimated and the determined at least oneproperty.
 19. The medical device of claim 15, further comprising: asurface scanner configured to scan the patient's surface anatomy togenerate the surrogate signal, wherein the radiotherapy treatment isupdated based on the surrogate signal and patient movement model. 20.(canceled)
 21. The medical device of claim 1, further comprising: animaging apparatus comprising a source of imaging radiation andconfigured to obtain the one or more initial images and the at least onesubsequent image.
 21. (canceled)
 22. (canceled)
 23. The medical deviceof claim 21, the controller circuitry being further configured to:receive a plurality of images, each image of the plurality of imagesdepicting at least part of the region and being taken at a differentpoint in the patient's respiratory cycle; and determine the range ofmotion of the region based on the plurality of images.
 24. The medicaldevice of claim 1, wherein the motion event time is associated with afirst region, and the controller circuitry is further configured to:determine a motion of a second region of the body of the patient basedon the one or more initial images; predict, based on the determinedmotion of the second region, a motion event time associated with thesecond region, at which time at least one property associated with themotion or position of the second region will meet at least one criterionassociated with the second region; and determine, based on the predictedmotion event time associated with the first region and the predictedmotion event time associated with the second region, the at least onesubsequent image capture time at which the subsequent image should becaptured.
 25. A method for tracking movement of a region of a body of apatient performable by a medical device, the region of the body of thepatient having a range of motion, the method comprising: determining amotion of the region based on one or more initial images depicting atleast part of the region; predicting, based on the determined motion, amotion event time at which at least one property associated with themotion or position of the region will meet at least one criterion,wherein the at least one criterion comprises the region being located ata particular point in its range of motion; and determining, based on thepredicted motion event time, at least one subsequent image capture timeat which at least one subsequent image should be captured. 26.-49.(canceled)
 50. A non-transitory computer readable medium comprisingcomputer executable instructions which, when executed by a processor ofthe computer, cause the computer to: determine a motion of a region of abody of a patient based on one or more initial images depicting at leastpart of the region; predict, based on the determined motion, a motionevent time at which at least one property associated with the motion orposition of the region will meet at least one criterion, wherein the atleast one criterion comprises the region being located at a particularpoint in its range of motion; and determine, based on the predictedmotion event time, at least one subsequent image capture time at whichat least one subsequent image should be captured.
 51. A medical devicefor tracking movement of an internal region of a body of a patient, thedevice comprising a controller, the controller including controllercircuitry configured to: generate a patient movement model based on: aplurality of images depicting at least part of the region, the pluralityof images being taken using an imaging apparatus comprising a source ofimaging radiation; and a surrogate signal indicative of movement of asurface anatomy of the patient; wherein the patient movement modelenables estimation of a position of the region as a function of thesurrogate signal and/or as a function of time; the controller furtherbeing configured to: estimate, using the patient movement model, amotion event time at which the position of the region will meet at leastone criterion; and determine, based on the estimated motion event time,at least one subsequent image capture time at which at least onesubsequent image should be captured using the imaging apparatus. 52.-61.(canceled)
 62. The device of claim 1, wherein the range of motion isassociated with a respiratory cycle of the patient and, wherein theparticular point in the range of motion of the region is a turning pointof the respiratory cycle.